diff options
| -rw-r--r-- | yaksh/models.py | 33 | ||||
| -rw-r--r-- | yaksh/views.py | 46 |
2 files changed, 51 insertions, 28 deletions
diff --git a/yaksh/models.py b/yaksh/models.py index 984a712..9153367 100644 --- a/yaksh/models.py +++ b/yaksh/models.py @@ -2134,11 +2134,38 @@ class AnswerPaperManager(models.Manager): best_attempt = 0.0 papers = self.filter(question_paper__quiz_id=quiz.id, course_id=course_id, - user=user_id).values("marks_obtained") - if papers: - best_attempt = max([marks["marks_obtained"] for marks in papers]) + user=user_id).order_by("-marks_obtained").values( + "marks_obtained") + if papers.exists(): + best_attempt = papers[0]["marks_obtained"] return best_attempt + def get_user_scores(self, question_papers, user, course_id): + qp_ids = list(zip(*question_papers))[0] + papers = self.filter( + course_id=course_id, user_id=user.get("id"), + question_paper__id__in=qp_ids + ).values("question_paper_id", "marks_obtained") + df = pd.DataFrame(papers) + user_marks = 0 + ap_data = None + if not df.empty: + ap_data = df.groupby("question_paper_id").tail(1) + for qp_id, quiz, quiz_marks in question_papers: + if ap_data is not None: + qp = ap_data['question_paper_id'].to_list() + marks = ap_data['marks_obtained'].to_list() + if qp_id in qp: + idx = qp.index(qp_id) + user_marks += marks[idx] + user[f"{quiz}-{quiz_marks}-Marks"] = marks[idx] + else: + user[f"{quiz}-{quiz_marks}-Marks"] = 0 + else: + user[f"{quiz}-{quiz_marks}-Marks"] = 0 + user.pop("id") + user["total_marks"] = user_marks + ############################################################################### class AnswerPaper(models.Model): diff --git a/yaksh/views.py b/yaksh/views.py index dc4f833..1530cd6 100644 --- a/yaksh/views.py +++ b/yaksh/views.py @@ -2256,40 +2256,36 @@ def download_course_csv(request, course_id): user = request.user if not is_moderator(user): raise Http404('You are not allowed to view this page!') - course = Course.objects.prefetch_related("learning_module").get( - id=course_id) + course = get_object_or_404( + Course.objects.prefetch_related("learning_module"), id=course_id + ) if not course.is_creator(user) and not course.is_teacher(user): - raise Http404('The question paper does not belong to your course') - students = course.get_only_students().annotate( + raise Http404('You are not allowed to view this course') + students = list(course.get_only_students().annotate( roll_number=F('profile__roll_number'), institute=F('profile__institute') ).values( - "id", "first_name", "last_name", - "email", "institute", "roll_number" - ) - quizzes = course.get_quizzes() - + "id", "first_name", "last_name", "email", "institute", "roll_number" + )) + que_pprs = [ + quiz.questionpaper_set.values( + "id", "quiz__description", "total_marks")[0] + for quiz in course.get_quizzes() + ] + total_course_marks = sum([qp.get("total_marks", 0) for qp in que_pprs]) + qp_ids = [ + (qp.get("id"), qp.get("quiz__description"), qp.get("total_marks")) + for qp in que_pprs + ] for student in students: - total_course_marks = 0.0 user_course_marks = 0.0 - for quiz in quizzes: - quiz_best_marks = AnswerPaper.objects. \ - get_user_best_of_attempts_marks(quiz, student["id"], course_id) - user_course_marks += quiz_best_marks - total_course_marks += quiz.questionpaper_set.values_list( - "total_marks", flat=True)[0] - student["{}".format(quiz.description)] = quiz_best_marks - student["total_scored"] = user_course_marks + AnswerPaper.objects.get_user_scores(qp_ids, student, course_id) student["out_of"] = total_course_marks + df = pd.DataFrame(students) response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename="{0}.csv"'.format( - (course.name).lower().replace('.', '')) - header = ['first_name', 'last_name', "roll_number", "email", "institute"]\ - + [quiz.description for quiz in quizzes] + ['total_scored', 'out_of'] - writer = csv.DictWriter(response, fieldnames=header, extrasaction='ignore') - writer.writeheader() - for student in students: - writer.writerow(student) + (course.name).lower().replace(' ', '_')) + output_file = df.to_csv(response, index=False) return response |